# Ultralytics YOLO 🚀, AGPL-3.0 license

from yolocode.yolov8.engine.predictor import BasePredictor
from yolocode.yolov8.engine.results import Results
from yolocode.yolov8.utils import ops


class DetectionPredictor(BasePredictor):
    """
    A class extending the BasePredictor class for prediction based on a detection model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.detect import DetectionPredictor

        args = dict(model='yolov8n.pt', source=ASSETS)
        predictor = DetectionPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def postprocess(self, preds, img, orig_imgs):
        """Post-processes predictions and returns a list of Results objects."""
        preds = ops.non_max_suppression(
            preds,
            self.args.conf,
            self.args.iou,
            agnostic=self.args.agnostic_nms,
            max_det=self.args.max_det,
            classes=self.args.classes,
        )

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
            img_path = self.batch[0][i]
            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
        return results

if __name__ == "__main__":
    predictor = DetectionPredictor()
    predictor.args.imgsz = 640
    predictor.setup_model(model=r"E:\YOLO\YOLOSHOW\ptfiles\yolov8n.pt")
    predictor.args.data = 'E:\YOLO\YOLOGUI\yolocode\yolov8\cfg\datasets\coco.yaml'
    predictor.predict_cli(source=r"D:\ChromeDownload\VideoTest\shortcut for 20s.mp4")